Quantitative Analysis of Commodity Markets, Household Vulnerability, and Learning Outcomes

Chapter 1 examines alternative specifications of futures-based forecasting models to improve upon existing approaches constrained by restrictive assumptions and limited information sets. We replace historical averages with rolling regressions and incorporate current market information through the deviation of the current basis from its historical average. To address potential non-stationarity and structural changes in the cash-futures price relationship, we employ a five-year rolling estimation window. Our findings indicate that the rolling regression approach yields significant improvements in both accuracy and information content of cotton season-average price forecasts, primarily at short forecast horizons. Chapter 2 addresses challenges in vulnerability assessment for semi-arid regions dependent on rainfed agriculture, where extreme weather events pose significant risks to household livelihoods. Despite advancements in remotely sensed technology, accurately estimating weather variability's impact on household livelihoods remains challenging. This study evaluates the effects of weather anomaly measures, spatial resolutions (i.e., geographic level at which the weather anomaly measures are evaluated), and household characteristics on household likelihood of falling into poverty (i.e., vulnerability) estimates. Combining household consumption data for Niger with remotely sensed agro-environmental measures, we find significant variations in vulnerability estimates based on the use of various weather condition measures (3 percentage points, equivalent to 600,000 households), spatial resolutions (8 percentage points, totalling 1.6 million households), and household characteristics (10 percentage points, equivalent to approximately 2 million households). Chapter 3 evaluates student learning outcomes from student involvement in hands-on learning settings, specifically focusing on student-managed investment funds. To assess the changes in the obtained technical and practical skills, we combine knowledge tests with grading rubrics. As part of practical skills, we consider commodity market analysis, critical thinking, informed decision-making, and insightful interpretation of market analysis results. We evaluate our students' understanding of commodity markets and their practical trading skills before and after joining the student-managed investment fund program. We find significant improvements in student learning outcomes, with students showing an average increase of 28% in disciplinary or technical knowledge and 38% in practical skills. Our findings highlight the importance of hands-on learning experiences to bridge the gap between theoretical knowledge and real-world application and in developing the well-rounded skill set demanded by the job market.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Economics, Agriculture and Life Sciences

Department dc:contributor.department

Economics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Poghosyan, Armine

Chairs dc:contributor.committeechair

• Benami, Elinor
• Isengildina Massa, Olga

Committee members dc:contributor.committeemember

• Stewart, Shamar L.
• Mills, Bradford F.

• forecast evaluation
• satellite remote sensing
• gridded weather
• experiential learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41235

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/120985